Overview
Course material
We will use the book
- [ML] Machine Learning, From the Classics to Deep Networks, Transformers, and Diffusion Models, 3rd edition, by Sergios Theodoridis, 2025. Download online from DTU Findit, or, the book can be purchased in polyteknisk bookstore at 10% discount.
As background material for the digital signal processing parts, we will use
Course outline by lecture module
| Week | Topic | Material (ML) |
|---|---|---|
| 1 | Digital signal processing, probability theory, machine learning | 1.1–2.3 |
| 2 | Matrix derivatives, constrained optimization, parameter estimation | 3.1–3.3, 3.5, 3.8–3.11, A.1–A.2, C.1–C.2 |
| 3 | Linear filtering | 2.4, 4.1–4.3, 4.5–4.7 |
| 4 | Adaptive filtering, LMS | 2.6, 5.1–5.5.1, 5.9, 5.12 |
| 5 | Adaptive filtering, RLS | 6.1–6.3, 6.5–6.8, 6.12 |
| 6 | Sparsity aware learning | 8.2, 8.10.1–8.10.2, 9.1–9.5, 9.9 |
| 7 | Shrinkage algorithms, Time-frequency analysis | 10.1–10.2, 10.5–10.6 |
| 8 | Dictionary learning, ICA, k-svd | 2.5, 19.1–19.3, 19.5–19.7 |
| 9 | Bayesian Modeling and EM | 11.2, 12.1–12.2, 12.4–12.5, 12.10 |
| 10 | State-space models, Hidden Markov models | 15.1–15.3.1, 15.7, 16.4–16.5 |
| 11 | State-space models, Kalman filter | 4.9–4.9.1, 4.10, 17.3 |
| 12 | Kernel methods, Kernel ridge regression | 11.1–11.5, 11.7 |
| 13 | Kernel methods, Support vector regression | 11.8 |